The Importance of Responsible AI Technology in Public Safety
Artificial intelligence (AI) is reshaping so much of what we do in many different sectors, and public safety is no exception. As AI adoption continues to grow, it presents both opportunities and challenges.
Using AI responsibly is crucial to harness its benefits while mitigating risks and biases. This blog post explores considerations for where to use different types of AI, the importance of pre-existing models, and compliance considerations related to the Department of Justice Criminal Justice Information Services (DOJ CJIS) framework.
Generative AI: How Can it be Practically Used in Public Safety?
There are several areas where Generative AI (GenAI) can be useful:
Data Analysis: GenAI can analyze vast datasets to identify patterns and trends, providing insights that aid in strategic decision-making. This can be particularly useful in identifying crime hotspots, clusters of similar events based on historical data, and reviewing large amounts of video, photos, or text.
Training Simulations: Creating realistic training scenarios for law enforcement officers can enhance preparedness and response capabilities. GenAI can simulate many diverse situations, helping officers practice and improve their skills in a controlled environment.
Public Communication: Drafting non-sensitive communications and public relations content can be streamlined with GenAI. This ensures timely and consistent messaging to the public without compromising sensitive public safety information.
Of course, alongside its useful applications, there are areas where GenAI should be approached with caution:
Critical Decision-Making: GenAI should be carefully evaluated for use in real-time decisions in critical situations. Though GenAI systems have made strides in reducing inaccuracies, hallucinations (providing responses that are not true or accurate), and biases, they can potentially lead to unintended consequences. Because of this, having a human-in-the-loop remains essential. Decisions in high-stakes scenarios require the judgment and experience that only human officers can provide, but can be enriched with AI.
Sensitive Data Processing: Handling Criminal Justice Information (CJI) with GenAI poses significant risks. The DOJ CJIS framework mandates stringent security measures to protect CJI. Using GenAI, especially cloud-based solutions, may violate these regulations and compromise data security. Building isolated and air-gapped models is required when using CJI.
Cost Consideration: Before implementing GenAI into existing software environments, it’s essential to assess the associated costs. Government agencies have a fiduciary responsibility to taxpayers to ensure that any AI integration is both realistic and within a reasonable budget. Careful budgeting and planning can help balance the benefits of AI with fiscal responsibility.
The Value of Non-Generative Models
While large language models (LLMs) and other GenAI systems are gaining popularity, established non-generative models still hold significant value. Traditional statistical models and rule-based systems offer several advantages:
Accuracy and Reliability: Non-generative models provide more predictable and reliable outputs. They operate on well-defined algorithms and rules, reducing the chances of unexpected behavior or errors
Specific Use Cases: In areas like precision policing, evidence-based policing, resource allocation, and crime mapping, non-generative models excel. Their accuracy in these applications often surpasses that of GenAI, making them indispensable tools for law enforcement agencies.
Speed and Cost: Traditional machine learning models are often very time and cost efficient and it should be a consideration when trying to choose a model that fits your specific use case.
Compliance with DOJ CJIS Framework
Adhering to the DOJ CJIS framework is critical for law enforcement agencies to protect CJI. Here are some key considerations:
Data Privacy Concerns: The CJIS framework outlines strict guidelines for handling CJI to ensure data privacy and security. Non-compliance can lead to severe penalties and loss of public trust.
Cloud AI Services: While cloud-based AI services offer scalability and efficiency, they pose significant risks to CJI security. Sending CJI to cloud providers not compliant with CJIS regulations can result in data breaches and legal violations.
Best Practices: To ensure compliance, agencies should use on-premises solutions or CJIS-compliant cloud services. This approach mitigates risks and ensures that sensitive information remains secure.
Insights from the Executive Order on AI
The Executive Order on Safe, Secure, and Trustworthy Development and Use of AI provides valuable guidance for responsible AI deployment. Key highlights include:
Safety and Security: AI systems must undergo rigorous, standardized evaluations to ensure safety and security. This includes pre-deployment testing, continuous monitoring, and implementing safeguards against misuse.
Promoting Innovation and Competition: The order encourages investments in AI education, training, and development while ensuring fair competition. Small developers and entrepreneurs are vital for driving innovation in AI technologies.
Protecting Civil Rights and Privacy: AI must not exacerbate discrimination or bias. Protecting privacy and civil liberties is paramount, requiring robust technical evaluations and careful oversight.
AI Use Cases in Law Enforcement
AI's applications in law enforcement are vast and varied, enhancing various aspects of public safety technology. Some current examples include:
Crime Detection and Prevention: AI can analyze data from surveillance cameras, sensors, and other sources to detect potential criminal activities. Precision policing models help identify high-risk areas, allowing for proactive measures.
Crime Investigations: AI aids in faster and more accurate investigations. Technologies like DNA analysis, facial recognition, and forensic analysis expedite the investigative process and improve accuracy.
Management and Accountability: AI can optimize technology in police operations and promote transparency. Officer wellness monitoring and early intervention systems analyze body camera footage to identify potential issues and recommend interventions.
Navigating Responsible AI’s Role
Since our inception in 2020, we have consistently focused on a balanced approach to AI in public safety. Leveraging GenAI can enhance data analysis and training, while non-generative models provide more reliable and precise outputs for critical applications. Ensuring compliance with the DOJ CJIS framework and following best practices for AI deployment will help law enforcement agencies harness AI's potential responsibly and safely.
However, it is critical to understand that the generic use of "AI" in marketing and media can be misleading or confusing. Often, AI is portrayed as a catch-all solution without the necessary context, leading to misconceptions about its capabilities and limitations. Both generative and non-generative AI models are not infallible; they are prone to errors and can sometimes make decisions that are difficult to interpret. For the public, it's essential to recognize the differences between various types of AI and to comprehend their unique applications and limitations, as well as the potential trade-offs, to fully understand the technology's impact on public safety.
By providing clear, contextual information and being transparent about AI's role and capabilities, public safety agencies can build trust and ensure that AI is used effectively and ethically.
By staying informed and adopting responsible practices, public safety agencies can maximize the benefits of AI while safeguarding public trust and security.
Public Safety Tech Trends This specialized AI model is designed to provide insights and analysis on the latest trends in public safety technology, drawing from public safety technology outlets dating back to 2020.
-1920w.png)
